International Global Navigation Satellite Systems Association IGNSS Symposium 2020

Colombo Theatres, Kensington Campus, UNSW Australia

5 – 7 February 2020

A Comparative Evaluation of Wi-Fi RTT and GPS Based Positioning

Yuntian Brian Bai (1) School of Science, RMIT University, Australia

yuntianbrian.bai@rmit.edu.au

Allison Kealy (2) School of Science, RMIT University, Australia

allison.kealy@rmit.edu.au

Guenther Retscher (3) Dept of Geodesy and Geoinformation, Vienna University of Technology, Austria

Guenther.Retscher@geo.tuwien.ac.at

Lucas Hoden (4) School of Science, RMIT University, Australia

lucas.hoden@rmit.edu.au

ABSTRACT

Wi-Fi-based positioning technology has snowballed over the past 20 years

along with the fast development and applications of smartphones for indoor

positioning. On the other hand, Wi-Fi is increasingly accepted for outdoor

positioning due to the availability and popularity of public Wi-Fi in global

cities. Since GPS signals are often interrupted and unstable in the downtown

areas with high-rise surrounded, Wi-Fi becomes an ideal positioning

technology as a substitution of GPS. Especially after the release of the IEEE

802.11mc standard last year, researchers and specialists from industries were

attracted immediately after the release. The new standard provides a fine time

measurement protocol for us to use multiple round-trip time (RTT) rather than

the received signal strength indicator (RSSI) for calculating the distance

between a Wi-Fi access point (AP) and a mobile end-user device. This paper

presents an evaluation and comparison study between Wi-Fi RTT and GPS

based localisations in an outdoor space located in a central downtown area in

Melbourne city. Based on the same testing environment and the same testing

points within a central city area, both GPS and Wi-Fi RTT are tested and

analysed. Results showed that the average positioning accuracies from the two

technologies are 5.10 m and 1.40 m, respectively. The Wi-Fi RTT technology

demonstrated a much better performance both in accuracy and stability.

KEYWORDS: GPS, Wi-Fi RTT, positioning, smartphone, LBS.

1. INTRODUCTION

The recent release of the IEEE 802.11mc standard provides us with a new era for smartphone

and Wi-Fi-based localisation. As the single most popular wireless network protocol of the 21st

century, Wi-Fi technology powers not only most home and business wireless networks, but also

public hotspot networks (Mitchell 2019; Ta 2018). Wi-Fi and smartphone-related location-

based service (LBS) and indoor positioning have gained much attention from both research and

industrial communities in the recent ten years (Donovan 2013; Machowinski 2013; Elkhodr,

Shahrestani, and Cheung 2016; Mohapatra, Choudhury, and Das 2014; Adams 2018; Gao,

Tang, and Bai 2019; Bai et al. 2014; Bai 2016).

The advantage of the 802.11mc standard for localisation is that supports a fine-time-

measurement (FTM) protocol, which allows us to calculate the distance between a smartphone

and an AP using round-trip-time (RTT) of the Wi-Fi signal transmission between a smartphone

and an AP. Applying the Wi-Fi RTT protocol leads to the increment of the positioning accuracy

from 5-10 meters as obtained from traditional positioning methods to about 1 meter in any line-

of-sight (LoS) surrounding environment (Diggelen, Want, and Wang 2018). This has brought

us a great era in using smartphone-based LBS, as both hardware standard and Android

application programming interfaces (APIs) are simultaneously evolving to enable an improved

ranging accuracy that has not previously been possible when using smartphones and Wi-Fi.

The rest of the paper is outlined as follows: the principle of the Wi-Fi RTT protocol will be

introduced in Section II. Section III will discuss the combination and conversions between

geodetic and local coordinate systems and also the Wi-Fi-based multilateration process. Section

IV will present the procedure of experimental tests and results analysis. Finally, the conclusion

part and future work will be addressed in Section V.

2. HOW DOES WI-FI RTT WORKS

The Wi-Fi 802.11 standard provides a possible way of achieving high-accuracy positioning in

a dense multipath environment, which imposes several hardware design changes in the existing

WLAN chipsets in order to increase the timing resolution from the microseconds level to the

nanosecond level (or even sub-nanosecond level) (Diggelen, Want, and Wang 2018). The Wi-

Fi RTT is a point-to-point (P2P) single-user protocol, which includes an exchange of multiple

message frames between an initiating station (ISTA) and a responding station (RSTA). The

ISTA (e.g., a smartphone) attempts to measure its range to the RSTA (e.g., an AP). Obtaining

an accurate time-delay estimate in a dense-multipath environment is challenging. It requires

precise detection of the first signal path associated with the LoS condition between the two

stations and the estimation of its arrival time (Banin et al. 2017; Yu et al. 2019). That is why

the RTT protocol not entirely compatible with a non-line-of-sight (NLoS) surrounding

environment currently. While Wi-Fi RTT protocol enables distance ranging between a

smartphone and an AP, the whole procedure is described as follows. First, the smartphone sends

an FTM request to the AP, then, the AP receives the request and returns an acknowledgement

(ACK) signal to the phone terminal. After that, several FTM feedbacks are sent from the AP to

the mobile terminal, and, then, the mean round-trip time is used for range calculation. This

process can also be performed between several smartphones and Wi-Fi APs at the “same” time.

The whole FTM RTT procedure is shown in Figure 1, in which the number of RTT (also called

“burst number”) can be changed to improve the FTM accuracy by providing multiple

measurements within one period. Currently, the default number of RTT is 8 and the maximum

number of successful measurements is 7.

Figure 1. Principle of the FTM protocol

The mean RTT of each period is calculated by Equation (1) (Yu et al. 2019):

𝑡𝑅𝑇𝑇 = 1

𝑁 (∑ 𝑡4_𝑖

𝑁

𝑖=1− ∑ 𝑡1_𝑖

𝑁

𝑖=1) −

1

𝑁 (∑ 𝑡3_𝑖

𝑁

𝑖=1− ∑ 𝑡2_𝑖

𝑁

𝑖=1) (1)

where:

𝑡1_𝑖 is the timestamp when the FTM framework first sent by a Wi-Fi AP;

𝑡2_𝑖 is the timestamp when the FTM signal arrives at the smartphone;

𝑡3_𝑖 is the timestamp when the smartphone returns the ACK signal to the AP;

𝑡4_𝑖 is the timestamp when the ACK signal is finally received by the AP;

𝑁 is the successful burst number (where 𝑁 > 0, 𝑁 < 𝐵 ); and

B is the total burst number (i.e., burst size, B = 8 by default in this research).

Generally, the protocol excludes the processing time on the smartphone terminal by subtracting

(t_(3_i) - t_(2_i)) from the total round-trip time (t_(4_i) - t_(1_i)), which represents the time

from the instant the FTM message is sent (t_(1_i)) to the instant, the ACK is received (t_(4_i)).

This calculation is repeated for each FTM-ACK exchange, and the final RTT is the average

over the successful number of FTM-ACK per burst. The estimated range can be obtained

through Equation (2).

Estimated Distance: Dest =1

2∗ 𝑡𝑅𝑇𝑇 ∗ 𝑐 (2)

The precondition for a smartphone to support Wi-Fi RTT is that the Android Pie (or called

Android P) operating system (OS) installed on it, which provides a number of APIs and allows

a developer to add RTT methods in their own application. The application needs to declare the

ACCESS_FINE_LOCATION permission, and both location and Wi-Fi scanning need to be

enabled on the end-user device [14].

One disadvantage of the Wi-Fi RTT so far is that it is hard to find many devices fully supporting

the RTT protocol. The RTT-based ranging requires supports from both the ISTA and RSTA

sides, which means all the devices must implement the 802.11mc standard (Android 2018).

Although many smartphone manufacturers announced that their products support the RTT

standard, e.g., the Essential, Nokia, OPPO, VIVO, Sony, and Xiaomi smartphones, none of

them (except the Pixel phones) has successfully tested and recorded by researchers as a device

supporting the Wi-Fi RTT protocol. From a simple test conducted by us, both OPPO Reno 5G

and VIVO X27 are not supporting the Wi-Fi RTT (see Figure 2) and only the Pixel 3 phone

supports the new protocol. For the RSTA devices, only the CompuLab WILD Wi-Fi RTT router

was formally announced so far to support the Wi-Fi RTT protocol. However, a few Google APs

were also successfully configured by us to do so.

Figure 2. Results of Wi-Fi RTT supporting test from three different smartphones

3. METHODOLOGY 3.1 Conversions between different coordinate systems

A universal geographic coordinate system needs to be established for both positioning systems

in order to compare the positioning accuracy between GPS and Wi-Fi RTT. Firstly, the east,

north, up (ENU) Cartesian coordinate system is defined according to the vertical and horizontal

dimensions from the latitudes and longitudes received, earth-fixed (ECEF, also known as earth-

centred rotational (ECR)) coordinate system. The ENU coordinates are formed from a plane

tangent to the Earth's surface fixed to a specific location and hence it is sometimes known as a

local tangent or local geodetic plane (see Figure 3). The relationship between ENU and ECEF

coordinate systems is shown in Figure 3.

Figure 3. Relationships between the local ENU and the ECEF coordinate systems

A local coordinate system called 𝐸′𝑁′𝑈′ Cartesian coordinate system is also established based

on the 𝐸𝑁𝑈 coordinate system, and the only difference is that the 𝐸′𝑁′𝑈′system reset the

values of E, N and U to 0 as a new initial point of the 𝐸′𝑁′𝑈′ system. Then, another local

coordinate system is also established as the Wi-Fi localisation coordinate system. The Wi-Fi

coordinate system (presented as X, Y and Z) is set to the same initial point with the

𝐸′𝑁′𝑈′system but with an angle (θ) as shown in Figure 4. In this case, let 𝑍 = 𝑈′, so only 2D

coordinate systems are displayed.

Figure 4. Relationship between the local 𝐸′𝑁′ and XY 2D coordinate systems

Generally, the coordinates received by smartphones are geodetic coordinates. The comparison

of the GPS estimates and Wi-Fi estimates require to convert the geodetic coordinates to 𝐸𝑁𝑈

coordinates, then to the 𝐸′𝑁′𝑈′ coordinates, which is usually in a three-stage process:

1. Convert geodetic coordinates to 𝐸𝐶𝐸𝐹 coordinates

2. Convert 𝐸𝐶𝐸𝐹 coordinates to 𝐸𝑁𝑈 coordinates

3. Convert 𝐸𝑁𝑈 coordinates to 𝐸′𝑁′𝑈′coordinates

The above conversion processes can be summarized as:

(𝜙, 𝜆, ℎ) => (𝑋𝑒𝑐𝑒𝑓, 𝑌𝑒𝑐𝑒𝑓 , 𝑍𝑒𝑐𝑒𝑓)

=> (𝐸, 𝑁, 𝑈)

=> (𝐸′, 𝑁′, 𝑈′)

In summary, a geodetic coordinate received from GPS is converted to a local coordinate system.

Equation (04) is used for the conversion between 𝐸𝑁𝑈 and 𝐸′𝑁′𝑈′ coordinates.

{𝐸 = 𝐸′ + 𝐸0

𝑁 = 𝑁′ + 𝑁0 (3)

Accuracies can be compared between [𝐸′

𝑁′]s from the GPS and the positions from local Wi-Fi

RTT.

3.2 Wi-Fi RTT-based positioning processes

Multilateration and simplified least squares (LS), for estimating the position from Wi-Fi RTT.

The estimated distances from smartphones need to be calibrated using an experimental

correction value, which can be obtained from an initial evaluation test.

Four APs with the strongest values of received signal strength indicator (RSSI) is selected for

conducting the LS process if there are more than 4 APs connected.

Compared to the latitudes and longitudes received, the height values form GPS is less accurate;

on the other hand, the height of the APs usually are constant values. Therefore, only 2D

coordinates are concerned in this research. All 3D distance values are simplified to 2D values,

which means only X and Y coordinates are considered for the multilateration process.

Assuming P(x, y) is the target position of a TP to be estimated. The exact formulas are:

𝑑𝑖2 = (𝑥 − 𝑥i)2 + (𝑦 − 𝑦i)2 (4)

or: 𝑥2 + 𝑦2 − 2𝑥i 𝑥 − 2𝑦i 𝑦 = 𝑑𝑖2 − 𝑥𝑖

2 − 𝑦𝑖2 (5)

Let 𝑝 = 𝑥2 + 𝑦2, and

𝑋 = [𝑝 𝑥 𝑦]𝑇

B = [1 −2𝑥1 −2𝑦1

⋮ ⋮ ⋮1 −2𝑥𝑛 −2𝑦𝑛

]

L = [𝑑1

2 − 𝑥12 − 𝑦1

2

⋮𝑑𝑛

2 − 𝑥𝑛2 − 𝑦𝑛

2]

where: 𝑖 = 1, 2, … , 𝑛 and 𝑛 = 3 𝑜𝑟 4. 𝑋 can be calculated by:

𝑋 = (𝐵𝑇𝐵)−1 𝐵𝑇 𝐿 (6)

Finally, the coordinates of the point P(x, y) can be obtained from Equation (12).

4. TEST & ANALYSIS 4.1Testbed establishment

As shown in Figure 5a, an area of about 30 x 25 𝑚2, located in the RMIT Alumni Courtyard

behind the Old Gaol, was selected as the testing area. Twelve testing points (TPs) were marked

on the ground, and the distances in X and Y directions to each adjacent points are all 6 metres

as shown in Figure 5b. The 4 APs are placed in the same height level with the smartphone so

that the height values for both the APs and the smartphone were omitted during the

multilateration process. The coordinates of the 4 APs in the local XY coordinates are shown in

Table 1.

Table 1. Coordinates of the 4 APs

(a) Testing area: RMIT’s Alumni Courtyard behind the Old Gaol

(b) The 12 TPs and 4 APs located in the testing area

Figure 5. Testbed establishment in city campus of the RMIT University

The Leica GS18 receiver (see Figure 6), a high precision GNSS receiver, was firstly used for

determining the coordinates of 4 base points (TPs 1, 3, 10 and 12) as their true coordinates, then

other true coordinates of the 8 TPs and 4 APs were defined accordingly with the assistance of

a total station and a tape.

Figure 6. Definition of the true coordinates for the 12 TPs

A Pixel 3 smartphone was used for collecting the data from GPS and the Wi-Fi APs. Two

Android APPs called GPS coordinates and Wi-Fi RTT Scan (see Figure 7) were used

respectively for collecting the latitude and longitude data and RTT data at each TP. Five pairs

of data were collected at each TP, and the average values were used as the location estimation

process. The time interval between any two adjacent collections is 2 seconds.

Figure 7. APPs used for colleting the GPS and Wi-Fi RTT data

4.2 Process and analysis of the received data

After the latitudes and longitudes of the 4 base TPs were obtained from the Leica GNSS

receiver, the true coordinates of the other 8 TPs can be easily defined by transforming the local

XY coordinates to EN coordinates with known bearing (i.e., θ, see Figure 4). In this research,

θ is defined as -14.437°, calculated from the true coordinates of TP1 and TP12. The received

latitudes and longitudes from the Android APP need to be converted to the grid coordinates for

the accuracy comparison process later. All the data including the true EN coordinates received

latitudes and longitudes, and the EN coordinates converted from the latitudes and longitudes,

as well as the accuracies for the 12 TPs, are listed in Table2.

It is noticeable that all the accuracy values for the first 4 TPs are more than 11.917 m and then

immediately reduced to the level of 1.824 to 7.226 m. The possible reason for this is that the

GPS coordinates APP might not work in a stable mode at the beginning of 1.5 minutes or other

disturbances, for example, the user had to stand by the smartphone for initial operation and the

user’s body might partly block the GPS signals. Without consideration of the first four accuracy

values, an average accuracy of 5.10 m was obtained from the rest 8 TPs for the GPS-based

positioning, which is as the average level as expected. All these accuracy values are displayed

in Table 2 and Figure 8.

Table 2. Process of the data received from GPS

Figure 8. Positioning accuracies based on the data received from GPS

While collecting the data from GPS, Wi-Fi RTT ranging data were also collected in the same

period. For each TP, the four ranging values received from the 4 APs need to be calibrated by

adding a deviation correction value (i.e., -3.348 m) according to the research results from (Bai

and Kealy 2019), then, they were applied to a multilateration process through Equations (10)

to (12). The accuracy value for the TP was obtained based on its estimated local X and Y

coordinates. The results and relevant data are displayed in Table 3 and Figure 9.

Table 3. Process of the data received from the Wi-Fi APs

Figure 9. Positioning accuracies based on the data received from the Wi-Fi APs

There are still some issues that need to be considered for the above positioning estimation

process. Apart from the unstable mode of the smartphone at the beginning of 1.5 minutes, other

factors also exist to affect the positioning accuracies, such as multi-path effect, effects from the

people walking around in the area during the test, and the smartphone user, who have to stand

by the phone in a very near distance to operate the phone.

The abovementioned test results show that positioning with Wi-Fi RTT can obtain much better

accuracy than with GPS. As Wi-Fi services are more and more common and free available in

many world-wide cities, location-based service (LBS) with Wi-Fi applications will become

more and more popular and vital for people’s daily activities.

3. CONCLUSIONS

This paper presents an evaluation and comparison study of GPS and Wi-Fi RTT based

localisation using a smartphone as the end-user device. The experimental test was conducted in

a relatively open area of the town centre in Melbourne. Relevant data from both GPS and Wi-

Fi RTT are collected simultaneously from the same TPs, and other user environmental factors

for both GPS and Wi-Fi are also the same. Results showed that the average accuracy for Wi-Fi

RTT based localisation is 1.40 m and much better than the accuracy obtained from the GPS.

This result is also firmly promising compared with the traditional way of Wi-Fi and

smartphone-based localisation results.

Implementation and comparison of these technologies in a real-time kinematic mode will

become the next step of our research plan.

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